Semantic Labeling by Maximum Entropy Model
Shan He, Daniel Gildea · UR Research (University of Rochester) · 2004
In this paper, we present the results for semantic labeling, extending the work of [Gildea and Jurafsky, 2002], [Fleischman et al., 2003], [Pradhan et al., 2004], and others. The main labeling approach is based on Maximum Entroopy. We show the performance of the baseline system as well as those by applying coreference resolution, stemming and feature combinations to the feature files.